Using Danish as a CG Interlingua: 
A Wide-Coverage Norwegian-English Machine 
Translation System 
Eckhard Bick Lars Nygaard
Institute of Language and 
Communication
The Text Laboratory
University of Southern Denmark University of Oslo
Odense, Denmark Oslo, Norway
eckhard.bick@mail.dk lars.nygaard@iln.uio.no
Abstract
This paper presents a rule-based 
Norwegian-English  MT  system. 
Exploiting  the  closeness  of 
Norwegian and Danish, and the 
existence  of  a  well-performing 
Danish-English system, Danish is 
used  as  an  «interlingua». 
Structural analysis and polysemy 
resolution are based on Constraint 
Grammar (CG) function tags and 
dependency  structures.  We 
describe  the  semiautomatic 
construction  of  the  necessary 
Norwegian-Danish dictionary and 
evaluate the method used as well 
as the coverage of the lexicon.
1 Introduction
Machine translation (MT) is no longer 
an unpractical science. Especially the 
advent of corpora with hundreds of 
millions  of  words  and  advanced 
machine learning techniques, bilingual 
electronic data and advanced machine 
learning techniques have fueled a 
torrent of MT-project for a large number 
of  language  pairs.  However,  the 
potentially most powerful, deep rule-
based approaches still struggle, for 
most  languages,  with  a  serious 
coverage  problem  when  used  on 
running, mixed domain text. Also, some 
languages, like English, German and 
Japanese, are more equal than others, 
not  least  in  a  funding-heavy 
environment like MT. 
The focus of this paper will be 
threefold: Firstly, the system presented 
here is targeting one of the small, 
«unequal»  languages,  Norwegian. 
Secondly, the method used to create a 
Norwegian-English  translator,  is 
ressource-economical in that it uses 
another, very similar language, Danish, 
as an «interlingua» in the sense of 
translation knowledge recycling (Paul 
2001), but with the recycling step at the 
SL side rather than the TL side. Thirdly, 
we will discuss an unusual analysis and 
transfer  methodology  based  on 
Constraint  Grammar  dependency 
parsing. In short, we set out to 
construct  a  Norwegian-English  MT 
system  by  building  a  smaller, 
Norwegian-Danish one and piping its 
output into an existing Danish deep 
parser (DanGram, Bick 2003) and an 
existing,  robust  Danish-English  MT 
system (Dan2Eng, Bick 2006 and 2007).
2 The MT system
The  Bokmål  standard  variety  of 
Norwegian is a language historically so 
close to Danish, that speakers of one 
language can understand texts in the 
Joakim Nivre, Heiki-Jaan Kaalep, Kadri Muischnek and Mare Koit (Eds.)
NODALIDA 2007 Conference Proceedings, pp. 21–28
Eckhard Bick and Lars Nygaard
other without prior training - though the 
same  does not necessarily hold for the 
spoken varieties. It is therefore a less 
challenging task to create a Norwegian-
Danish MT system than a Norwegian-
English or even Norwegian-Japanese 
one. Furthermore, syntactic differences 
are so few, that lexical transfer can to a 
large degree be handled at the word 
level with only part of speech (PoS) 
disambiguation  and  no  syntactic 
disambiguation, allowing us to depend 
on the Danish parser to provide a deep 
structural analysis. Furthermore, the 
polysemy spectrum of many Bokmål 
words closely matches the semantics of 
the corresponding Danish word, so 
different English translation equivalents 
can be chosen using Danish context-
based discriminators.
2.1 Norwegian analysis
As a first step of analysis, we use the 
Oslo-Bergen Tagger (Hagen et al. 2000) 
to provide lemma disambiguation and 
PoS tagging, the idea being to translate 
results into Danish, using a large 
bilingual lexicon, and feed them into the 
syntactic and dependency stages of the 
DanGram parser. However, though both 
the OBT tagger and DanGram adhere to 
the Constraint Grammar (CG) formalism 
(Karlsson  1990),  a  number  of 
descriptive compatibility issues had to 
be addressed. Since categories could 
not always be mapped one-to-one, we 
had to also use the otherwise to-be-
skipped syntactic stage of the OBT 
tagger in order to further disambiguate 
a word's part of speech. Thus, the 
Danish preposition-adverb distinction is 
underspecified in the Norwegian system 
where the 2 lexemes have the same 
form, using the preposition tag even 
without the presence of a pp. The same 
holds for about 50 words that in Danish 
are regarded as unambiguous adverbs, 
but in Norwegian as unambiguous 
prepositions.
2.2 The Norwegian-Danish lexicon
The complexity of a Norwegian-Danish 
dictionary can be compared to Spanish-
Catalan language pair addressed in the 
open source Apertium MT project 
(Corbí-Bellot et al. 2005), where a 1-to-1 
lexicon was deemed sufficient (with a 
few polysemous cases handled as multi-
word  expressions),  avoiding  the 
disambiguation complexity of many-to-
many lexica necessary for less-related 
languages.  Even  without  extensive 
polysemy mismatches, the productive 
compounding nature of Scandinavian 
languages, however, increases lexical 
complexity as compared to Romance 
languages - an issue reflected in the 
transfer evaluation in chapter 2.3.
In a project with virtually zero 
funding, like ours, it can be difficult to 
build or buy a lexicon, not to mention 
the general lack of wide-coverage 
Norwegian-Danish electronic lexica to 
begin with. So with only a few thousand 
words from terminology lists or the like 
available, creative methods had to be 
employed,  and  we  opted  for  a 
bootstrapping system with the following 
steps:
(a)  Create  a  large  corpus  of 
monolingual  - Norwegian text and 
lemmatize it automatically. Quality  was 
less important in this step, since 
frequency measures could be employed 
to weed out errors and create a 
candidate list of Norwegian lemmas.
(b) Regard Norwegian as misspelled 
Danish, and run a Danish spell checker 
on the lemma-list obtained from (a). 
Assume translation as identical, if the 
Norwegian word is accepted by a 
Danish spell checker. Use correction 
suggestions  by  spell  checkers  as 
translations  suggestions.  Because 
differences  could be greater than 
Levenshtein distance 1 or 2, a special, 
CG-based spell checker (OrdRet, Bick 
2006) was used, with a particular focus 
on heavy, dyslexic spelling deviations 
22
Using Danish as a CG Interlingua: A Wide-Coverage Norwegian-English Machine Translation System
and  a  mixed  graphical-phonetic 
approach.
(c) Produce phonetic transmutation 
rules for Norwegian and Danish spelling 
to generate hypothetical Danish words 
from Norwegian candidates, and than 
check if a word of the relevant word 
class was listed in either DanGram's 
parsing lexicon or its spell checker 
fullform list.
Methods (a-c) resulted in a list of 
226,000  lemmas  with  translations 
candidates in Danish. Only 20,000 low-
frequency  words  were  completely 
unmatchable. In a first round of manual 
revision, all closed-class words, all 
polylexical matches were checked, and 
a confidence value from DanGram's 
spell checker module was used to grade 
suggestions into safe, unsafe and none. 
Next, a compound analyzer was written 
and run on all Norwegian words, 
accepting compound splits as likely if 
the resulting parts both individually 
existed in the word list, finally creating 
a  Danish  translation  from  the 
translations of the parts, and checking 
it and its epenthetic letters against the 
Danish lexicon. This step not only 
helped to fill in remaining blanks, but 
was also used to corroborate spell 
checker suggestions as correct, if they 
matched the translation produced by 
compound analysis - or replace them, if 
not. After this, 13.800 lemmas had no 
translation, 23.500 lemmas were left 
with an «unsafe» marking from the spell 
checker stage, and in 20.700 cases, 
compound analysis contradicted spell 
checker or list suggestions otherwise 
deemed safe.  Allowing overrides in the 
latter case, and removing the two 
former cases, we were left with a 
bilingual lemma list of 188.500 entries.
Finally,  a  dual  pass  of  manual 
checking was directed at all items with 
a  frequency  count  over  10, 
corresponding  to  about  12.5%.  In 
obvious cases, related low-frequency 
words in neighbouring positions on the 
alphabetical list were corrected at the 
same time.
In order to evaluate our method of 
lexicon-creation, we extracted all words 
with frequency 9 - the most frequent 
group without prior manual revision - 
and inspected all suggested translations 
(1544 cases). 
type n %
non-word 33 2.1 %
wrong PoS 8 0.5 %
   etymology 
=
161 10.4 %
   transpare
nt1
6 0.4
   intranspar
ent2
20 1.3 %
all 
corrected
187 12.1 %
all 228 14.8 %
Table 1
As can be seen from table 1, ignoring 
the 2.6 % of non-words from the corpus-
based lemma-list, about 12% of the 
unrevised translations were wrong. 
However, in most of these cases (10.4%, 
over 4/5), the Danish translations were 
still etymologically - and thus spelling-
wise  - related to their Norwegian 
1 brise (blæse), spenntak (spændloft), 
stabbe, strupetak (strubelåg), villastrøk 
(villakvarter), vårluft (forårsluft)
2 guttete (drengete), havert (slags sæl), 
hengemyr (hængedynd), kraftsektor 
(energisektor), koring, kvinneyrke, 
langdryg, låtskriver, lønnsnemnd, 
malingflekk, omvisning (rundvisning), purke 
(so), sauebonde, smokk (sut), strikkegenser, 
søppelbøtte (affaldsbøtte), tukle (fumle), 
tøyelig (fleksibel), vassdrag (vandløb), 
yrkesutdanning
23
Eckhard Bick and Lars Nygaard
counterparts,  and  should  thus  be 
accessible  to  improved  automatic 
matching-techniques.
frequen
cy
non-
word
wrong 
PoS
correct
ed
9 (all) 2.1 % 0.5 % 12.1 %
5 0.5 % 1.5 % 14 %
4 4 % 0.5 % 14 %
3 1 % 0.5 % 10.5 %
2 4 % 3 % 9.5 %
1 3.5 % 0.5 % 8.5 %
average 2.5 % 1.1 % 11.4 %
Table 2
Small checks were also conducted 
for other frequencies (200 words each), 
randomly extracting 1 out of 10 words. 
Results  indicate  that  automatic 
translatability  remains  similar   in 
general, though
there was a slight correlation between 
falling frequency and  less  need for 
correction. The proportion of non-words 
was high for low frequencies, possibly 
reflecting spelling errors and analysis 
problems with rare words in the corpus 
data.  However,  since  having  non-
existing words in the SL-list,  is only 
«noise» and not a problem for the MT 
system,  we  conclude  from  their 
translatability that low-frequency words 
are at least as safe a contribution to the 
lexicon as high-frequency words.  
2.3 Norwegian-Danish transfer 
Analysed input from the Oslo-Bergen-
tagger  is  danified  by  substituting 
Danish base forms for Norwegian ones. 
Even with an extensive bilingual word 
list, the transfer program is not, 
however, a mere lookup procedure. Due 
to the compounding structure of the 
languages involved, compound analysis 
has to be performed both on the 
Norwegian and the Danish side - the 
former  to  achieve  a  part-by-part 
translation for words not listed in the 
bilingual lexicon, the latter to permit 
assignment  of  secondary  Danish 
information  (valency,  semantics)  to 
Danish translations not covered by the 
DanGram monolingual lexicon.
The  Norwegian-Danish  transfer 
module was evaluated on 1,000 mixed-
genre sentences from the Norwegian 
web part  of the Leipzig Corpora 
Collection3 and a 6.500 word chunk 
from the ECIcorpus4.
Web Litterature
words 15,641 6,521
N, ADJ, V, 
ADV 
8,976 
(57.4%)
3,098 
(47.5%)
  not in noda-
lex
991 (6.3%) 182 (2.8%)
    compound
s
458 (2.9%) 78 (1.2%)
  not in dan-
lex
127 (0.8%) 32 (0.5%)
Table 3
The failure rate for Norwegian words 
was 6.3% in the web corpus, in part 
compensated by the fact that almost 
half of these (2.9%) could still be 
compound-analyzed. The coverage rate 
of the Danish lexicon was very high - 
only 0.8% of suggested translations 
were  not  found.  Figures  for  the 
literature corpus were almost twice as 
good - even when taking into account 
that  the  percentage  of  open-class 
inflecting words was 10 percentage 
points lower in this corpus.
2.4 Danish generation
Finally, Danish full-forms are generated 
from the translated base-forms, based 
3 http://corpora.uni-leipzig.de
4 European Corpus Initiative, 
http://www.elsnet.org/resources/eciCorpus.html
24
Using Danish as a CG Interlingua: A Wide-Coverage Norwegian-English Machine Translation System
both on the filtered OBT morphological 
tag string, and inflexional information 
from the Danish lexicon. 
"[hus]  N  NEU  S  DEF  GEN", for 
instance, will be inflected as hus  -> 
NEU  DEF  huset  ->  GEN  husets. 
Irregular forms are stored in full in a 
separate file, and compound stems are 
constructed, prior to inflexion, using 
rules for the insertion of epenthetic s or 
epenthetic e.
(1) agurk+tid -> agurketid
(2) forbud+stat -> forbudsstat
Alas,  Danish  and  Norwegian 
morphology  are  not  completely 
isomorphic, and in order to handle 
differences in a context-dependent way, 
a special CG grammar is run before 
generation. This grammar handles, for 
instance, the Norwegian phenomenon 
of double definiteness:
(3) NOR:  den store bilen ->  DAN: 
den store bil
Here, so-called substitution rules are 
used, replacing the tag DEF with IDF in 
the  presence  of  definite  articles 
(example below) or pre- or post-
positioned determiners and attributes 
(syntactic  tags  @<ADJ,  @<DET, 
@ADJ>, @DET>):
SUBSTITUTE (DEF) (IDF) TARGET 
(N) 
IF (*-1 ART BARRIER NON-PRE-
N/ADV) ;
2.5 Structural analysis
Syntactic-functional analysis was based 
not on the Norwegian OBT-analysis, but 
on a from-scratch analysis of the 
translated Danish text, in part because 
of the high syntactic accuracy of the 
Danish parser (Bick 2000), in part to 
ensure  compatibility  with  the 
descriptive conventions used in the next 
syntactic stage, dependency analysis, 
and the Danish-English MT system 
itself. The Dependency grammar in 
question (described in Bick  2005) 
consists  of  a  few  hundred  rules 
targeting CG function tags, supported 
by attachment direction markers and 
close/long-attachment markers from a 
special CG layer run as a last step 
before dependency.
2.6 Danish-English transfer
Though the Danish-English MT system 
(Dan2eng, fBick 2007) is not the focus 
of this paper, and used as is in a black 
box fashion, a short description is in 
order  - not least because of the 
perspective of ultimately creating a 
similar system for direct Norwegian-
English transfer.
The core principle of Dan2eng is to 
rely as much as possible on deep and 
accurate SL analysis. In this spirit, the 
selection of translation equivalents is 
based  on  lexical  transfer  rules 
exploiting  syntactic  relations  in  a 
semanticised way. The way in which 
Dan2eng semanticizes syntax, differs 
significantly from many older rule-
based MT systems designed in the 80's 
and 90's. First, it uses dependency 
rather than constituent analyses, and 
second, it is the first MT system ever to 
be based on Constraint Grammar, a 
combination that provides it with a 
robust way of progressing from shallow 
to deep analyses (Bick 2005) without 
the high percentage of parse failures 
inherent to many generative systems 
when run on free text5.  
As an example, let us have a look at 
the translation spectrum Danish verb at 
regne (to rain),  which has many other, 
non-meteorological,  meanings 
(calculate, consider, expect, convert ...) 
as well. Here, Dan2eng simply uses 
grammatical  distinctors  to distinguish 
between  translations,  rather  than 
define sub-senses.
5 Even today, MT systems using deep syntax, may 
find it cautious to restrict their domain or 
structural scope, like the LFG- and HPSG-based 
LOGON system (Lønning et al. 2004).
25
Eckhard Bick and Lars Nygaard
Thus, the translation  rain  (a)  is 
chosen if a daughter/dependent (D) 
exists  with  the  function  of 
situative/formal  subject  (@S-SUBJ), 
while most other meanings ask for a 
human subject. As a default6 translation 
for the latter calculate (f) is chosen, but 
the presence of other  dependents 
(objects or particles) may trigger other 
translations.  regne  med  (c-e),  for 
instance, will mean include, if med has 
been identified as an adverb, while the 
preposition  med  triggers  the 
translations  count  on  for  human 
«granddaughter» dependents (GD = 
<H>), and expect otherwise. Note that 
the include  translation also could have 
been conditioned by the presence of an 
object  (D = @ACC), but would then 
have to be differentiated from (b), 
regne for (‘consider’).
regne_V7
(a) D=(@S-SUBJ) :rain; 
(b) D=(<H> @ACC) D=("for" PRP)_nil 
:consider; 
(c) D=("med" PRP)_on GD=(<H>) :count; 
(d) D=("med" PRP)_on :expect; 
(e) D=(@ACC) D=("med" ADV)_nil 
:include; 
(f) D=(<H> @SUBJ) D?=("på" PRP)_nil 
:calculate; 
The example shows how information 
from different descriptive layers is 
integrated  in  the  transfer  rules. 
Structural conditions may either be 
expressed in n-gram fashion (with P+n 
or P-n) positions, or dependency fashion 
(reference  to  daughters,  mothers, 
granddaughters  and  grandmothers 
independent  of distance).  Semantic 
conditions can either be inferred with 
6 The ordering of differentiator-translation pairs 
is important - readings with fewer restrictions 
have to come last. The example lacks the 
general, differentiator-free default provided with 
all real lexicon entries.
7 The full list of differentiators for this verb 
contains 13 cases, including several 
prepositional complements not included here 
(regne efter, blandt, fra, om, sammen, ud, fejl ...)
regular expressions from word or base 
forms, or exploit DanGram's semantic 
prototype tags in a systematic way, e.g. 
<tool>, <container>, <food>, <Hprof> 
etc. for nouns (160 types in all). 
Adjectives and verbs have fewer classes 
(e.g. psychological adjective, move, 
speech or cognitive verbs), but make up 
for this with a rich annotation of 
argument/valency tags.
The rule-based transfer system is 
supplemented by a dictionary of fixed 
expressions and a (so far sentence-
based) translation memory. The Danish-
English bilingual lexicon was built to 
match the coverage of the DanGram 
lexicon (100.000 words plus 40.000 
names), but does not yet have the same 
coverage for compounds. In any case, 
compounds  are  productive,  and 
therefore covered by a special back-up 
module that combines part-translations, 
affix-translations. Rules may be used to 
force a different translation for a 
lexeme if used as first or second part in 
compounds,  e.g.  FN-styrke,  where 
styrke should be 'force', not 'strength'. 
The  compound  module  is  doubly 
important for our Nor2eng interlingua 
approach,  since  secondary  Danish 
lookup-failures  may  be  caused  by 
Norwegian lookup-failures.
2.7 English generation and syntax
English generation is handled much like 
Danish generation, drawing on CG 
morphological  tags,  a  lexicon  of 
irregular  forms  and  some 
phonetic/stress  heuristics  to  inflect 
translated base forms - again supported 
by a special CG layer performing 
systematic substitutions (for instance 
plural translations of singular words) 
and insertions (certain modals, or 
articles). Differences in syntax are 
handled by successive transformation 
rules, which may move either words or 
whole dependency tree sections if 
26
Using Danish as a CG Interlingua: A Wide-Coverage Norwegian-English Machine Translation System
certain tags, tokens or sequences are 
found.
In  the  following  example,  two 
movement rules were applied. The first 
changes the Scandinavian VS order into 
SV after a filled front field, placing the 
fronted adverbial between S and V. The 
other rule, classifying the adverbial, 
decides on a better place for it  - 
between auxiliary and main verb. 
NOR: På 1980-tallet ble sammenhengen 
mellom sosiale faktorer og helse i stor 
grad avskrevet.
DAN: 
I PRP @ADVL #1->13
1980'erne N @P< #2->1
blev V @STA
#3->0
sammenhængen N  @SUBJ
#4->3
mellem PRP @N< #5->4
sociale ADJ @>N #6->7
faktorer <cjt1> N @P<
#7->5
og KC @CO
#8->7
helse <cjt2> N @P< #9->7
i PRP @ADVL #10-
>13
stor ADJ @>N #11-
>12
grad N @P< #12-
>10
afskrevet V @AUX< #13-
>3.
ENG: In  the  1980s  the  connexion 
between social factors and health was 
largely written off.
Note  also  the  fact,  that  the 
preposition change is a difference 
between Norwegian and Danish, not 
between Danish and English, and that 
the subject movement acted on the 
whole NP, including its dependent PP, 
which again contained a coordination. 
The necessary dependency links are 
marked  in  the  Danish  interlingua 
sentence.
Illustration 1
3 Perspectives:  Statistical 
smoothing 
In spite of the fact that Dan2Eng 
employs tens of thousands of hand-
written lexical transfer rules, it is 
extremely  difficult  to  cover  all 
idiosyncrasies  of,  for  instance, 
preposition usage or choice of synonym 
in a rule based way. Furthermore, 
mismatches  are  more  likely  when 
chaining two translations. On the other 
hand, statistical methods allow to check 
the  probabilities  of  rule-suggested 
lex
lex
lex
lex
Norwegian
text
Danish
text
English
text
OB­tagger
(CG)
DanGram
* morphology
* disambig.CG
* syntax­CG 
Nor2dan
­ transfer
­generation
Dan2eng
­ transfer
­ generation
Adapt.
CG
Adapt.
CG
Dependency
grammar
Statistical
smoothing
VISL
adapt
27
Eckhard Bick and Lars Nygaard
translations  in  a  given  context, 
smoothing  out  translational  rough 
spots. Given the lack of large bilingual 
Norwegian-Danish  or  Norwegian-
English  corpora,  it  is  an  added 
advantage, that such methods work 
with  monolingual,  target  language 
corpora  - of which there are almost 
unlimited amounts availabe in the case 
of English. To prepare for an integration 
of  TL  smoothing,  we  performed 
dependency annotation of 1 billion 
words, and started extracting n-gram 
information as well as what we call dep-
grams  - hierarchical  chains  of 
dependency-linked words, the former 
with the perspective of preposition-
smoothing, the latter for argument-
smoothing.
Future evaluations, to be conducted 
after a more complete revision of the 
Norwegian bilingual lexicon and the 
construction of a polysemy-sensitive 
Norwegian-Danish transfer grammar, 
will have to address not only the overall 
quality of the MT system as a whole - 
optimally in comparison with other 
systems, like LOGON (Lønning et al. 
2004)  -,  but  also  the  relative 
contributions  of  rule  based  and 
statistical modules.
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